A subset of our analyses were carried out in a similar manner in earlier work.
[1]} tested different combinations of features with a decision tree classifier on ecbp. They find that system performance majorly stems from a lemma overlap feature and that adding discourse, entity coreference and wsd features only improves BLANC F1 by 1pp.
In parallel to our work, [2]} conducted a study in which they built a feature-based mention pair model for ecbp to gain deeper insights into the importance of features and on the performance impact of document preclustering. Amongst four features (fastText [3]} word embedding similarity between event actions, event action word distribution, sentence similarity and event action part-of-speech comparison), the embedding similarity feature was found to be the most important by far. The use of document preclustering caused an improvement of 3pp. CoNLL F1, leading [2]} to encourage future researchers in this field to report experiments with and without document preclustering.
